This code implements Spectral Clustering, more or less, as described here. The code uses shogun machine learning library for matrixes manipulation and kmeans.
There is no warranty that the code is error/bug free and valid. Purpose of the code is to provide a simple implementation of the algorithm.
- c++11
- qmake
- shogun
Make sure you have shogun installed (you can add by hand shogun's path in the .pro file, you can override what's already there).
git clone https://github.com/alamages/SpectralClustering.git
cd SpectralClustering
mkdir build && cd build
qmake ..
The code supports feature vectors and undirected unweighted graph clustering. Input vectors must be provided in libsvm format. The input graph must be provided in this format:
[node_id] [neighbor_id1] [neighbor_id2] ....
The node ids must appear in an increasing sequence starting from index 1 up to n (where n is the number of nodes). Same with the neighbor ids.
Examples of input files can be found in the data/ folder.
You can check the main.cpp for a simple example of how to use the code.
- make more tests
- probably add support for weighted/directed graph someday
- add class to provide custom graph similarity matrix for graph files